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Of the "we really don't like matplotlib syntax, really do want to live in dataframes all the time, so we wrapped mpl and pandas" plotting libraries, plotnine ha

Of the "we really don't like matplotlib syntax, really do want to live in dataframes all the time, so we wrapped mpl and pandas" plotting libraries, plotnine has impressed me the most by far: https://plotnine.readthedocs.io/en/stable/ https://plotnine.readthedocs.io/en/stable/ plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot.

uoaei 6y ago
What makes you choose this over something like seaborn?
- voxelghost 6y ago
  Not parent, obv. But the syntax (or grammar if you like) although jarring at first, is starting to grow on me. I am not fully onboard yet, but it is an interesting idea.
hantusk 6y ago
I'm really fond of https://altair-viz.github.io/ https://altair-viz.github.io/ which wraps and translates into https://vega.github.io/vega-lite/ https://vega.github.io/vega-lite/. The interactivity and cross-filtering possibilities is really good when you have many datapoints. From vega-lite I miss that they did not develop object constancy (https://bost.ocks.org/mike/constancy/ https://bost.ocks.org/mike/constancy/) animation possibilities since they're building on top of d3.js.
- stevesycombacct 6y ago
  I'm a big fan of Altair and have used it for demos in the past. However, restrictions such as a 5,000-row limit on dataframes and usage only inside notebooks pushes me to other solutions like Plotly.
  - pea 6y ago
    BTW, the 5000 restriction is more a "guide" for structuring your data - you can turn it off. Also, Altair plots can certainly be used outside of notebooks! Happy to share some examples..
    - stevesycombacct 6y ago
      Please, I couldn't find a straightforward configuration to do that
Myrmornis 6y ago
It looks like this one is designed specifically to be easy to learn and remember. It looks very useful to me for people who make plots less frequently.
globular-toast 6y ago
When I was in the business of making plots, ggplot2 was by far my favourite. It seemed odd at first, but eventually it clicked and it felt great. Once you grok the grammar of graphics you'll find you can really quickly express all kinds of plots from any given dataset. I would routinely impress people with how quickly I could visualise data.

uoaei 6y ago

What makes you choose this over something like seaborn?

voxelghost 6y ago
Not parent, obv. But the syntax (or grammar if you like) although jarring at first, is starting to grow on me. I am not fully onboard yet, but it is an interesting idea.

hantusk 6y ago

I'm really fond of https://altair-viz.github.io/ https://altair-viz.github.io/ which wraps and translates into https://vega.github.io/vega-lite/ https://vega.github.io/vega-lite/. The interactivity and cross-filtering possibilities is really good when you have many datapoints. From vega-lite I miss that they did not develop object constancy (https://bost.ocks.org/mike/constancy/ https://bost.ocks.org/mike/constancy/) animation possibilities since they're building on top of d3.js.

stevesycombacct 6y ago
I'm a big fan of Altair and have used it for demos in the past. However, restrictions such as a 5,000-row limit on dataframes and usage only inside notebooks pushes me to other solutions like Plotly.
- pea 6y ago
  BTW, the 5000 restriction is more a "guide" for structuring your data - you can turn it off. Also, Altair plots can certainly be used outside of notebooks! Happy to share some examples..
  - stevesycombacct 6y ago
    Please, I couldn't find a straightforward configuration to do that

Myrmornis 6y ago

It looks like this one is designed specifically to be easy to learn and remember. It looks very useful to me for people who make plots less frequently.

globular-toast 6y ago

When I was in the business of making plots, ggplot2 was by far my favourite. It seemed odd at first, but eventually it clicked and it felt great. Once you grok the grammar of graphics you'll find you can really quickly express all kinds of plots from any given dataset. I would routinely impress people with how quickly I could visualise data.